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Searching for an optimal hexagonal shaped enumeration unit size for effective spatial pattern recognition in choropleth maps / Izabela Karsznia in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 9 (September 2021) . - n° 576 Titre : Searching for an optimal hexagonal shaped enumeration unit size for effective spatial pattern recognition in choropleth maps Type de document : Article/Communication Auteurs : Izabela Karsznia, Auteur ; Izabela Golebiowska, Auteur ; Jolanta Korycka-Skorupa, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 576 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatiale [Termes IGN] carte choroplèthe [Termes IGN] carte thématique [Termes IGN] échelle cartographique [Termes IGN] enquête [Termes IGN] généralisation [Termes IGN] lecture de carte [Termes IGN] reconnaissance de formes [Termes IGN] répartition géographique [Termes IGN] représentation cartographique [Termes IGN] utilisateur [Termes IGN] visualisation cartographique Résumé : (auteur) Thoughtful consideration of the enumeration unit size in choropleth map design is important to ensure the correct communication of spatial information. However, the enumeration unit size and its influence on pattern conveying in choropleth maps have not yet been the subject of in-depth empirical studies. This research aims to address this gap. We focused on the issue concerning whether the ability to recognize spatial patterns on an Equal Area Unit Map is related to the hexagonal enumeration unit size, defined by the number of pixels. The aim is to indicate the range of the enumeration unit sizes, namely, at what point the upper and lower borders of the range where the spatial patterns start, and where the end is visible and recognizable by users. To address this problem, we conducted an empirical study with 488 users. The results show that the enumeration unit size has an impact on the users’ spatial pattern recognition abilities. Choropleth maps with enumeration unit sizes of 26, 52, and 104 pixels were, in the majority, indicated by participants as those most suitable for indicating spatial patterns. This was in contrast to choropleth maps with enumeration unit sizes of 1664 and 3328 pixels, which users indicated as not being useful. However, there were some exceptions to this general finding. Thus, determining the optimal enumeration unit size is a challenging task, and requires further insightful investigations. Numéro de notice : A2021-686 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10090576 date de publication en ligne : 25/08/2021 En ligne : https://doi.org/10.3390/ijgi10090576 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98412 [article]

Predicting user activity intensity using geographic interactions based on social media check-in data / Jing Li in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 8 (August 2021) . - n° 555 Titre : Predicting user activity intensity using geographic interactions based on social media check-in data Type de document : Article/Communication Auteurs : Jing Li, Auteur ; Wenyue Guo, Auteur ; Haiyan Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 555 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] données issues des réseaux sociaux [Termes IGN] interaction spatiale [Termes IGN] mobilité [Termes IGN] modèle non linéaire [Termes IGN] relation spatiale [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau neuronal récurrent [Termes IGN] utilisateur Résumé : (auteur) Predicting user activity intensity is crucial for various applications. However, existing studies have two main problems. First, as user activity intensity is nonstationary and nonlinear, traditional methods can hardly fit the nonlinear spatio-temporal relationships that characterize user mobility. Second, user movements between different areas are valuable, but have not been utilized for the construction of spatial relationships. Therefore, we propose a deep learning model, the geographical interactions-weighted graph convolutional network-gated recurrent unit (GGCN-GRU), which is good at fitting nonlinear spatio-temporal relationships and incorporates users’ geographic interactions to construct spatial relationships in the form of graphs as the input. The model consists of a graph convolutional network (GCN) and a gated recurrent unit (GRU). The GCN, which is efficient at processing graphs, extracts spatial features. These features are then input into the GRU, which extracts their temporal features. Finally, the GRU output is passed through a fully connected layer to obtain the predictions. We validated this model using a social media check-in dataset and found that the geographical interactions graph construction method performs better than the baselines. This indicates that our model is appropriate for fitting the complex nonlinear spatio-temporal relationships that characterize user mobility and helps improve prediction accuracy when considering geographic flows. Numéro de notice : A2021-588 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10080555 date de publication en ligne : 17/08/2021 En ligne : https://doi.org/10.3390/ijgi10080555 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98206 [article]

ComNet: combinational neural network for object detection in UAV-borne thermal images / Minglei Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 6662 - 6673 Titre : ComNet: combinational neural network for object detection in UAV-borne thermal images Type de document : Article/Communication Auteurs : Minglei Li, Auteur ; Xingke Zhao, Auteur ; Jiasong Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6662 - 6673 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] détection d'objet [Termes IGN] image captée par drone [Termes IGN] image thermique [Termes IGN] piéton [Termes IGN] saillance [Termes IGN] véhicule Résumé : (auteur) We propose a deep learning-based method for object detection in UAV-borne thermal images that have the capability of observing scenes in both day and night. Compared with visible images, thermal images have lower requirements for illumination conditions, but they typically have blurred edges and low contrast. Using a boundary-aware salient object detection network, we extract the saliency maps of the thermal images to improve the distinguishability. Thermal images are augmented with the corresponding saliency maps through channel replacement and pixel-level weighted fusion methods. Considering the limited computing power of UAV platforms, a lightweight combinational neural network ComNet is used as the core object detection method. The YOLOv3 model trained on the original images is used as a benchmark and compared with the proposed method. In the experiments, we analyze the detection performances of the ComNet models with different image fusion schemes. The experimental results show that the average precisions (APs) for pedestrian and vehicle detection have been improved by 2%~5% compared with the benchmark without saliency map fusion and MobileNetv2. The detection speed is increased by over 50%, while the model size is reduced by 58%. The results demonstrate that the proposed method provides a compromise model, which has application potential in UAV-borne detection tasks. Numéro de notice : A2021-632 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3029945 date de publication en ligne : 21/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3029945 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98288 [article]

Spatial knowledge acquisition with virtual semantic landmarks in mixed reality-based indoor navigation / Bing Liu in Cartography and Geographic Information Science, vol 48 n° 4 (July 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 4 (July 2021) . - pp 305 - 319 Titre : Spatial knowledge acquisition with virtual semantic landmarks in mixed reality-based indoor navigation Type de document : Article/Communication Auteurs : Bing Liu, Auteur ; Linfang Ding, Auteur ; Liqiu Meng, Auteur Année de publication : 2021 Article en page(s) : pp 305 - 319 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] conception orientée utilisateur [Termes IGN] GPS assisté pour la navigation (technologies) [Termes IGN] hologramme [Termes IGN] information sémantique [Termes IGN] navigation virtuelle [Termes IGN] point de repère [Termes IGN] positionnement en intérieur [Termes IGN] questionnaire [Termes IGN] réalité mixte [Termes IGN] téléphone intelligent [Termes IGN] utilisateur civil Résumé : (auteur) Landmarks are essential and widely used in human navigation. However, many indoor environments lack visually salient landmarks, which leads to difficulties in navigating in and learning complex and similar-looking indoor environments. In this study, we designed and implemented virtual semantic landmarks in Mixed Reality (MR)-based indoor environments and conducted a user study to explore whether such landmarks can assist spatial knowledge acquisition during navigation. More specifically, we employed the untethered, head-mounted mixed reality device Microsoft HoloLens and used iconic holograms to show the semantic landmarks. In the user study, we used sketch map, landmark locating tasks and interview to assess the results of the spatial knowledge acquisition and collect advice on improving the MR-based navigation interface. The results show that virtual semantic landmarks can assist the acquisition of corresponding knowledge, as such landmarks were labeled second most often in landmark locating task. In addition, individual cases show that head-mounted mixed reality devices may influence not only vision, but also height or time perception of certain users. Our result can be applied to facilitate the design of MR-based navigation interfaces and assist spatial knowledge acquisition. Numéro de notice : A2021-445 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1908171 date de publication en ligne : 22/04/2021 En ligne : https://doi.org/10.1080/15230406.2021.1908171 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97852 [article]

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Towards generating network of bikeways from Mapillary data / Xuan Ding in Computers, Environment and Urban Systems, vol 88 (July 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 88 (July 2021) . - n° 101632 Titre : Towards generating network of bikeways from Mapillary data Type de document : Article/Communication Auteurs : Xuan Ding, Auteur ; Hongchao Fan, Auteur ; Jianya Gong, Auteur Année de publication : 2021 Article en page(s) : n° 101632 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] approche participative [Termes IGN] cycliste [Termes IGN] données localisées des bénévoles [Termes IGN] gestion des itinéraires [Termes IGN] Mapillary [Termes IGN] OpenStreetMap [Termes IGN] Suède [Termes IGN] système d'information géographique Résumé : (auteur) Nowadays, biking is flourishing in many Western cities. While many roads are used for both cars and bicycles, buffered bike lanes are marked for the safety of cyclists. In many cities, segregated paths are built up to have physical separation from motor vehicles. These types of biking ways are regarded as attributes in geographic information system (GIS) data. This information is required and important in the service of route planning, as cyclists may prefer certain types of bikeways. This paper presents a framework for generating networks of bikeways with attribute information from the data collected on the collaborative street view data platform Mapillary. The framework consists of two layers: The first layer focuses on constructing a bikeway road network using Global Positioning System (GPS) information of Mapillary images. Mapillary sequences are classified into walking, cycling, driving (ordinary road), and driving (motorway) trajectories based on the transportation mode with a trained XGBoost classifier. The bikeway road network is then extracted from cycling and driving (ordinary road) trajectories using a raster-based method. The second layer focuses on extracting attribute information from Mapillary images. Cycling-specific information (i.e., bicycle signs/markings) is extracted using a two-stage detection and classification model. A series of quantitative evaluations based on a case study demonstrated the ability and potential of the framework for extracting bikeway road information to enrich the existing OSM cycling road data. Numéro de notice : A2021-432 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101632 date de publication en ligne : 17/04/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101632 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97798 [article]

Imaging the subsurface: How different visualizations of cross-sections affect the sense of uncertainty / Ane Bang-Kittilsen in Journal of Geovisualization and Spatial Analysis, vol 5 n° 1 (June 2021)  

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A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)  

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A user-driven process for INSPIRE-compliant land use database: example from Wallonia, Belgium / Benjamin Beaumont in Annals of GIS, vol 27 n° 2 (April 2021)  

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An experiment using the graphic variable color and the see color code on isarithmic maps accessible to blind and normally sighted people / Niédja Sodré de Araújo in Boletim de Ciências Geodésicas, vol 27 n° 1 ([01/03/2021])  

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An introduction to ethics in robotics and AI / Christoph Bartneck (2021)  

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Évaluation et spatialisation du potentiel offert par les moyens d'alerte centrés sur la localisation des individus / Esteban Bopp (2021)  

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Le carrefour dont vous êtes le héros : description de carrefours pour les personnes déficientes visuelles / Jérémy Kalsron (2021)    

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Using multi-agent simulation to predict natural crossing points for pedestrians and choose locations for mid-block crosswalks / Egor Smirrnov in Geo-spatial Information Science, vol 23 n° 4 (December 2020)  

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Machine‐learning prediction models for pedestrian traffic flow levels: Towards optimizing walking routes for blind pedestrians / Achituv Cohen in Transactions in GIS, Vol 24 n° 5 (October 2020)  

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Comparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

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